Camera-centric 3D-MPE tasks aim to recover each pose as coordinates in the camera-centric coordinate system. This requires estimating the absolute depth of each person in 3D. Generally, existing methods for 3D-MPE can be divided into two categories: top-down and bottom-up. Typical top-down approaches use the single-person method with a 2D person detector to handle multi-person scenes [1]}. This involves a human detector and pose estimation in two stages. Since each person in the image is treated individually, these methods ignore out-of-patch contexts. They also suffer from high computational redundancy. By contrast, bottom-up 3D-MPE [2]} does not need human detectors and can perceive global image contexts in a single shot. Thus, multi-person interactions in pose estimation tasks do not present a problem.
